Imperfect Model Research Articles

Image reconstruction with transformer for mask-based lensless imaging.

A mask-based lensless camera optically encodes the scene with a thin mask and reconstructs the image afterward. The improvement of image reconstruction is one of the most important subjects in lensless imaging. Conventional model-based reconstruction approaches, which leverage knowledge of the physical system, are susceptible to imperfect system modeling. Reconstruction with a pure data-driven deep neural network (DNN) avoids this limitation, thereby having potential to provide a better reconstruction quality. However, existing pure DNN reconstruction approaches for lensless imaging do not provide a better result than model-based approaches. We reveal that the multiplexing property in lensless optics makes global features essential in understanding the optically encoded pattern. Additionally, all existing DNN reconstruction approaches apply fully convolutional networks (FCNs) which are not efficient in global feature reasoning. With this analysis, for the first time to the best of our knowledge, a fully connected neural network with a transformer for image reconstruction is proposed. The proposed architecture is better in global feature reasoning, and hence enhances the reconstruction. The superiority of the proposed architecture is verified by comparing with the model-based and FCN-based approaches in an optical experiment.

The physiological basis for estimating photosynthesis from Chla fluorescence.

Solar-induced Chl fluorescence (SIF) offers the potential to curb large uncertainties in the estimation of photosynthesis across biomes and climates, and at different spatiotemporal scales. However, it remains unclear how SIF should be used to mechanistically estimate photosynthesis. In this study, we built a quantitative framework for the estimation of photosynthesis, based on a mechanistic light reaction model with the Chla fluorescence of Photosystem II (SIFPSII ) as an input (MLR-SIF). Utilizing 29 C3 and C4 plant species that are representative of major plant biomes across the globe, we confirmed the validity of this framework at the leaf level. The MLR-SIF model is capable of accurately reproducing photosynthesis for all C3 and C4 species under diverse light, temperature, and CO2 conditions. We further tested the robustness of the MLR-SIF model using Monte Carlo simulations, and found that photosynthesis estimates were much less sensitive to parameter uncertainties relative to the conventional Farquhar, von Caemmerer, Berry (FvCB) model because of the additional independent information contained in SIFPSII . Once inferred from direct observables of SIF, SIFPSII provides 'parameter savings' to the MLR-SIF model, compared to the mechanistically equivalent FvCB model, and thus avoids the uncertainties arising as a result of imperfect model parameterization. Our findings set the stage for future efforts to employ SIF mechanistically to improve photosynthesis estimates across a variety of scales, functional groups, and environmental conditions.

An imperfect thermal contact problem for consolidation of bilayered saturated soil subjected to ramp-type heating

This study proposes a generalized imperfect thermal contact model to simulate the thermal contact resistance existing at the interface between the two adjacent soil layers. Based on the generalized imperfect thermal contact model, a thermo-hydro-mechanical (THM) coupled consolidation model for bilayered saturated soil under ramp-type heating load is established. By means of the Laplace transform, the semi-analytical solutions of temperature increment, excess pore water pressure and displacement of the bilayered saturated soil are obtained. Based on the present solutions, the THM coupled consolidation behaviors are investigated through comparative and parametric analysis. The results show that the generalized imperfect thermal contact model can realistically describe the imperfect thermal contact problem and can be degenerated into other commonly-used thermal contact models. Due to the existence of interfacial thermal contact resistance, only a portion of heat flux passes through the interface, weakening the influence of the thermal load on the consolidation behaviors of non-heated layer. With the decrease of contact transfer coefficient and the increase of partition coefficient, the interfacial thermal contact resistance increases, and the influence of the thermal conductivity of soil grains reduces.

Numerical and Experimental Investigation of Quasi-Static Crushing Behaviors of Steel Tubular Structures.

In this study, a numerical and experimental investigation of the quasi-static crushing behavior of steel tubular structures was conducted. As the crushing failure behavior involves a high level of nonlinearity for the numerical simulations, these were compared with previous experimental works, including crushing tests of steel square tubes to calibrate the numerical results. Six parameters for the numerical simulations, namely (1) loading boundary condition, (2) geometrical imperfection, (3) friction coefficient, (4) element size, (5) element type, and (6) material nonlinearity model, were examined using a series of finite element analyses. Through the sensitivity study for each parameter, the deformation and crushing load of the steel tube were investigated, and the value that best matched the experimental results was selected. The results of the numerical analysis for the determined model were compared with the experimental results. Finally, the authors provided recommendations that should be considered when performing nonlinear finite element simulations of crushing failure events.

Urban Climate Risk Mitigation via Optimal Spatial Resource Allocation

Decision makers (DMs) who are involved in urban planning are often required to allocate finite resources (say, money) to improve outdoor thermal comfort (OTC) levels in a region (e.g., city, canton, country). In this paper, for the first time, we address the following two questions, which are directly related to this requirement: (1) How can the statistical properties of the spatial risk profile of an urban area from an OTC perspective be quantified, no matter which OTC index the DM chooses to use? (2) Given the risk profile, how much and where should the DM allocate the finite resources to improve the OTC levels? We answer these fundamental questions by developing a new and rigorous mathematical framework as well as a new class of models for spatial risk models. Our approach is based on methods from machine learning: first, a surrogate model of the OTC index that provides both accuracy and mathematical tractability is developed via regression analysis. Next, we incorporate the imperfect climate model and derive the statistical properties of the OTC index. We present the concept of spatio-temporal aggregate risk (STAR) measures and derive their statistical properties. Finally, building on our derivations, we develop a new algorithm for spatial resource allocation, which is useful for DMs and is based on modern portfolio theory. We implemented the tool and used it to illustrate its operation on a practical case of the large-scale area of Singapore using a WRF climate model.

Bending and torsional rigidities of defected femur bone using finite element method

The present work studies the bending and torsional rigidities for the femur when having a defect. The main objective of this study is to compare the stiffness of femur bone with and without defect for two different approaches of modeling. One of them considers the bone made of a singular piece and the second one has three pieces included of the cortical shaft and two cancellous. To this aim, the femur geometrical model is obtained by means of materialize Mimics and analyzed using the finite-element software package ABAQUS/Standard v. 6.14. Afterwards, some verifications are presented to show the accuracy of our simulation. From our point of view, considering the imperfect model with cortical shaft and two cancellous is closer to reality. It is mentioned that in our modeling, because of the complexity of the bone's geometry and the shape of defect, we had challenges such as convergency and accuracy of the results which will be discussed in the article. The current work shows that imperfection plays an important role in the statics of femoral bone analysis. In addition, the differences made from in stress analysis of single-part femur bone and the bone which is made of three different parts in the body, show the importance of the current work.

Interface imperfection effects on spin transfer torque switching: an atomistic approach

The further commercialization of spintronic memory devices depends on the development of methods by which to assess performance. This paper presents an approach to the atomistic investigation of switching performance in spin transfer torque magneto-resistive random access memory (MRAM) devices with the use of interface imperfection model. Switching simulation in the nanosecond regime was made possible under this model, and we first time demonstrate that switching time is inversely proportional to interface imperfection (i.e. roughness). In investigating the damping of CoFeB/MgO films, we analyzed the effective damping constant α eff, which cannot be accurately predicted for ferromagnetic layers of less than 2 nm using existing micromagnetic models. The proposed model includes a roughness parameter, which has nearly no effect on the effective damping constant in films of >2 nm, but a profound effect in films of <2 nm, reaching a 27% decrease in a 1.0 nm CoFeB film. Our finding is supported by the experimental data of classic references. We expect that these results will prove valuable in magnetic simulation and research on MRAM with ultrathin films.

Reconstructing Arctic Sea Ice over the Common Era Using Data Assimilation

Abstract Arctic sea ice decline in recent decades has been dramatic; however, few long-term records of Arctic sea ice exist to put such a decline in context. Here we employ an ensemble Kalman filter data assimilation approach to reconstruct Arctic sea ice concentration over the last two millennia by assimilating temperature-sensitive proxy records with ensembles drawn from last millennium climate model simulations. We first test the efficacy of this method using pseudoproxy experiments. Results show good agreement between the target and reconstructed total Arctic sea ice extent (R2 value and coefficient of efficiency values of 0.51 and 0.47 for perfect model experiments and 0.43 and 0.43 for imperfect model experiments). Imperfect model experiments indicate that the reconstructions inherit some bias from the model prior. We assimilate 487 temperature-sensitive proxy records with two climate model simulations to produce two gridded reconstructions of Arctic sea ice over the last two millennia. These reconstructions show good agreement with satellite observations between 1979 and 1999 CE for total Arctic sea ice extent with an R2 value and coefficient of efficiency of about 0.60 and 0.50, respectively, for both models. Regional quantities derived from these reconstructions show encouraging similarities with independent reconstructions and sea ice sensitive proxy records from the Barents Sea, Baffin Bay, and East Greenland Sea. The reconstructions show a positive trend in Arctic sea ice extent between around 750 and 1820 CE, and increases during years with large volcanic eruptions that persist for about 5 years. Trend analysis of total Arctic sea ice extent reveals that for time periods longer than 30 years, the satellite era decline in total Arctic sea ice extent is unprecedented over the last millennium. Significance Statement Areal coverage of Arctic sea ice is a critical aspect of the climate system that has been changing rapidly in recent decades. Prior to the advent of satellite observations, sparse observations of Arctic sea ice make it difficult to put the current changes in context. Here we reconstruct annual averages of Arctic sea ice coverage for the last two millennia by combining temperature-sensitive proxy records (i.e., ice cores, tree rings, and corals) with climate model simulations using a statistical technique called data assimilation. We find large interannual changes in Arctic sea ice coverage prior to 1850 that are associated with volcanic eruptions, with a steady rise in Arctic sea ice coverage between 750 and 1820 CE. The satellite-period loss of sea ice has no analog during the last millennium.

Heterogeneity of beliefs and information rigidity in the crude oil market: Evidence from survey data

This paper assesses information contained in the micro dataset of the ECB Survey of Professional Forecasters regarding quarterly Brent crude oil price forecasts. We examine the expectations building mechanism by referring to the processing of information and confirm the presence of information rigidity within the crude oil market. However, our findings also show that simple models of imperfect information considered in the literature are insufficient to explain the behavior of professional forecasters. We provide additional stylized facts which are helpful for designing more elaborate imperfect information models.

Coordinated operation method of cascade hydropower stations considering runoff error

The traditional collaborative scheduling method of cascade hydropower stations group has the problem of imperfect regression model, which leads to excessive water consumption rate. A collaborative scheduling method of cascade hydropower stations group considering runoff error is designed. The influence factors of runoff forecast were obtained, the network parameters were called, and the regression model was built based on the runoff error according to the total power generation of cascade hydropower stations in the degree period. The objective function of hydropower system with hydropower stations as the center was established to optimize the cooperative dispatching mode. Experimental results: The average water consumption rates of the coordinated operation method of cascade hydropower stations group in this paper and the other two kinds of coordinated operation method of cascade hydropower stations group are 34.86%, 57.26% and 59.47% respectively, indicating that the coordinated operation method of cascade hydropower stations group with runoff error is more perfect.

An application of control theory for imperfect production problem with carbon emission investment policy in interval environment

The theory of optimal control plays an important role to solve several real-life problems. However, it is often seen that uncertainty is a major challenge in formulating the appropriate model corresponding to real world problems. To overcome this challenge, in this work, the theory of optimal control is developed in interval environment. Accordingly, the necessary and sufficient optimality conditions for the existence of maximizer of interval valued optimal control problem (IVOCP) are proposed. To validate the proposed theory, an imperfect production inventory model is developed in both crisp and interval environments in which customers’ demand is dependent on the selling price, specific absorption rate (SAR) of products and continuous time. In the proposed model, the production rate is considered as a function of time. In addition, manufacturer invests to reduce the carbon emission and to increase the environmental sustainability of the produced items. As the production rate is taken as a function of time, optimal control theory is used to study the optimal policy of the corresponding problem. To solve the optimal control problem for the proposed model in interval environment, sufficient optimality conditions of IVOCP are used. Thereafter, two numerical examples are considered and solved with the help of different meta-heuristic algorithms. Finally, to observe the impact of various system parameters to the optimal policy of the system, sensitivity analyses are carried out for the example in interval environment and impacts are shown graphically.

Mathematical Models for Assessing the Reliability of Agricultural Machinery, Taking into Account the Influence of Military Factors in the Front-line Territory

As the requirements for the quality of agricultural machinery and its working conditions increase, so do the demands on its maintainability, in particular its reliability. Failure for technical reasons or due to any external factors will result in the non-achievement of the goals set, and thus in a loss of performance. Today, as a rule, there is an insufficient level of reliability of agricultural equipment during operation, and this issue receives special attention in the territories that are located in the front-line zone in the east of Ukraine in the Donetsk and Luhansk regions. This is caused by both design solutions and imperfect models that are used during projecting. In addition, external factors that can significantly affect production activities are not taken into account. These include climatic conditions, natural disasters, personnel readiness, i.e. the human factor, as well as the factor of the consequences of an armed conflict. Therefore, the purpose of the article is to offer a mathematical model for assessing the reliability of agricultural machinery, taking into account the military factor in the front-line territory. The authors provide an analytical expression of the mathematical model for assessing the reliability of agricultural machinery, taking into account the influence of external military factors. This issue is gaining special attention in the front-line territories in eastern Ukraine, where a significant part of the acreage is also located. The proposed mathematical model, in contrast to the previous ones, will allow taking into account the wear and time-related deterioration of components of agricultural machinery, as well as external military factors (damage to equipment and personnel, damage due to collision with munitions, etc.). This mathematical model can be used to set the uptime requirements for agricultural machinery in a more reasonable manner and thereby increase its productive performance. During the research, methods of probability theory were used at the stage of determining the necessary probabilities of trouble-free operation of agricultural machinery, as well as methods of queuing theory, reliability theory of technical systems and mathematical modelling when determining the maintenance time of agricultural machinery samples for failure under various conditions of its application

Chemomechanical behaviors of particle enforced heterogeneous composites with chemical interfacial jumps

Deep understanding of mass diffusion controlled chemomechanical behaviors of composites with local mass flux discontinuities between inclusions and matrixes are of great significance in modern advanced technology and engineering applications. This work develops a finite-deformation chemomechanical model for particle-enforced composites when under mass diffusion. A general chemomechanical cohesive model is developed for chemo-mechanically imperfect interfaces between the inclusions and matrixes. In the developed cohesive model, both of the displacement jump, the chemical potential jump as well as the mass flux jump are considered. A interfacial potential is developed upon the consideration that the diffusant permeation deteriorate interfacial fracture and degrade the interface strength. Applying standard variations procedure on total energy of the coupled chemomechanical system, the associated weak formulations are developed, and then implemented in finite element method software ABAQUS through subroutine UELs. To validate the proposed cohesive model, the mass-diffusion-driven crack propagation is simulated and compared with the published works. A series of numerical examples are conducted to elucidate the key features of chemo-mechanically imperfect interface model. The results show that the jump of mass flux across interface results in a normal swelling of interface even no mechanical load is applied. An increase of interface diffusion coefficient causes a lower chemical potential jump. Parametric analysis show that the interface properties, such as the cohesive strength, fracture energy and degradation parameter, affect the diffusion rate through controlling the mechanical performance of interface. A strong interface can restrict effectively the mass spread across the interface. This research provides a theoretical reference for a wide applications of particle-reinforced composites in chemical environment.

On The Robustness Of Respondent-Driven Sampling Estimators To Measurement Error.

Respondent-driven sampling (RDS) is a popular method of conducting surveys in hard to reach populations where strong assumptions are required in order to make valid statistical inferences. In this paper we investigate the assumption that network degrees are measured accurately by the RDS survey and find that there is likely significant measurement error present in typical studies. We prove that most RDS estimators remain consistent under an imperfect measurement model with little to no added bias, though the variance of the estimators does increase.

Multi-objective imperfect selective maintenance optimization for series-parallel systems with stochastic mission duration

To improve the probability that an engineering system successfully completes its next mission, it is crucial to implement timely maintenance activities, especially when maintenance time or maintenance resources are limited. Taking series-parallel system as the object of study, this paper develops a multi-objective imperfect selective maintenance optimization model. Among it, during the scheduled breaks, potential maintenance actions are implemented for the components, ranging from minimal repair to replacement. Considering that the level of maintenance actions is closely related to the maintenance cost, age reduction coefficient and hazard rate adjustment coefficient are taken into account. Moreover, improved hybrid hazard rate approach is adopted to describe the reliability improvement of the components, and the mission duration is regarded as a random variable. On this basis, a nonlinear stochastic optimization model is established with dual objectives to minimize the total maintenance cost and maximize the system reliability concurrently. The fast elitist non-dominated sorting genetic algorithm (NSGA-II) is adopted to solve the model. Numerical experiments are conducted to verify the effectiveness of the proposed approach. The results indicate that the proposed model can obtain better scheduling schemes for the maintenance resources, and more flexible maintenance plans are gained.

Effect of geometric irregularities induced during manufacturing of a crash-box on its crashworthiness performance

Imperfections associated with the geometric features of any component are inevitable during its fabrication and some of those can even have a drastic impact on its functionalities. One such case arises while fabricating one of the essential passive safety devices in an automobile called as 'thin-walled collapsible energy absorbers'. Thin-walled collapsible energy absorbers also known as crash-boxes are the tubular shell structures located in between the bumper and front side rails of an automobile. Crash-boxes collapse in a stable progressive manner to absorb kinetic energy of an external impact to protect the occupants during an accident. Tubular crash-boxes are generally manufactured by extrusion. Geometric imperfections in the form of irregularities in the shell thickness may be induced during extrusion of the crash-boxes. The present study aims to gauge the effects of irregularities present in the thickness of the extruded crash-box on it’s crashworthiness performance, numerically. Cylindrical aluminium alloy tubes are modelled as crash-boxes using ABAQUS/Explicit. The irregularities in the thickness of the tubular crash-box are modelled with the help of three different imperfect models. In every imperfect model, maximum variation in the thickness is kept within the tolerance value, provided by the standards DIN EN-755-9. The quasi-static axial collapse of these ‘imperfect’ cylindrical tubes is simulated and their crashworthiness performance indicators are compared with those of an ‘ideal’ circular crash-box. It is seen that the energy absorption and the mean crushing force reduced drastically due to these shell thickness irregularities in comparison to the ideal model. It is also observed that the mode of collapse of the circular tube changed from axi-symmetric concertina mode to the diamond mode due to irregularities in their thickness. Such significant change in the crashworthiness performance of crash-box may lead to a huge disaster in terms of human lives. Hence, crash-boxes should be fabricated carefully with more tight geometric and dimensional tolerances.

Kharitonov polynomial-based interval reduced order modelling of Cuk converter

This paper proposes a method of reduced order modelling for Cuk converter using state space averaging (SSA) technique in which combined state space description is obtained and output to control for Cuk converter transfer function is determined. However, there may be some variation in parameters of system due to uncertainties and imperfect modelling that are addressed using interval modelling. Thus, the obtained interval model is reduced further using Kharitonov polynomials. Routh-Padé approximation is employed for model order reduction. This procedure of model order reduction is explained for Cuk converter model. Fourth-order system is reduced to its respective first, second and third-order reduced models. To signify efficacy of technique proposed, frequency and step responses of models and system are plotted. Comparative study is performed using time domain specifications. Further, difference between responses of model and system are presented in terms of performance indices. The results prove effectiveness and simplicity of proposed technique.

Maintenance Decision Optimization of Multi-State System Considering Imperfect Maintenance

In the maintenance decision-making problem of a multi-state system, it is more in line with the actual situation to consider imperfect maintenance. However, under the influence of imperfect maintenance, the failure mechanism and maintenance process of the multi-state system are more complicated. Further discussion is needed about how to deal with the relationship between the states of various components and the overall state of the system, and how to make the optimal maintenance strategy. To address this issue, this paper proposes a multi-state system maintenance decision optimization method considering imperfect maintenance. Firstly, an imperfect maintenance model is established based on quasi-renewal theory. Secondly, the mapping relationship between the state of the components and the system is decoupled combining universal generating function with the non-homogeneous continuous-time Markov model, the reliability evaluation of the multi-state system is realized. Finally, the maintenance decision optimizing model considering imperfect maintenance is constructed, the optimal maintenance strategy of the multi-state system is obtained by optimizing the number of imperfect maintenance actions to maximize the expected benefit of the whole life cycle.

Airfoil design and surrogate modeling for performance prediction based on deep learning method

Aiming at the problems of a long design period and imperfect surrogate modeling in the field of airfoil design optimization, a convolutional neural network framework for airfoil design and performance prediction (DPCNN) is established based on the deep learning method. The airfoil profile parameterization, physical field prediction, and performance prediction are achieved. The results show that the DPCNN framework can generate substantial perfect airfoil profiles with only three geometric parameters. It has significant advantages such as good robustness, great convergence, fast computation speed, and high prediction accuracy compared with the conventional machine learning method. When the train size is 0.1, the predicted results can be obtained within 5 ms. The prediction absolute errors of physical field of most sample points are lower than 0.002, and the relative errors of aerodynamic performance parameters are lower than 2.5%. Finally, the optimization attempt of operating parameters is completed by gradient descent method, which shows good stability and convergence. Overall, the DPCNN framework in this paper has outstanding advantages in time cost and prediction accuracy.

Development of a framework to capture abstraction in physical chemistry problem solving

Productive problem solving, concept construction, and sense making occur through the core process of abstraction. Although the capacity for domain-general abstraction is developed at a young age, the role of abstraction in increasingly complex and disciplinary environments, such as those encountered in undergraduate STEM education, is not well understood. Undergraduate physical chemistry relies particularly heavily on abstraction because it uses many overlapping and imperfect mathematical models to represent and interpret phenomena occurring on multiple scales; however, studying and identifying abstraction in-the-moment in physical chemistry is challenging, because current conceptions of abstraction neglect the domain-specific features. This work uses an approach guided by informed grounded theory to develop a conceptual framework that makes abstraction in physical chemistry problem solving visible. Problem solving teaching interviews with individuals and pairs (n = 18) on thermodynamics and kinetics topics are analyzed using an abductive approach. The resulting Epistemic Actions of Abstraction framework characterizes eight epistemic actions along two dimensions: increasing abstractness relative to the context (concretizing, manipulating, restructuring, and generalizing) and nature of the object the action operates on (conceptual or symbolic). These actions are used to identify two types of abstraction: horizontal and vertical abstraction. We discuss how abstraction in problem solving is contextually dependent and implications this work has for problem solving in physical chemistry, as well as implications for physical chemistry instruction.